The lack of high-throughput phenotypic data is hindering our ability to describe the metabolic potential of microbes based on their genome annotation and our ability to predict gene complements from phenotypic analyses. To effectively understand microbes and their interaction with the environment, we need to be able to predict their metabolic capabilities. Conversely, if we detect a phenotypic change in microbes, such as increased copper tolerance, we need to be able to predict the genes that are responsible.
The overall goal of this project is to improve our caability to reliaby predict phenotype from genotype for microbial life across the bacterial kingdom. this projec will result in genomic sequences, annotation and microbial metabolic models. To adress this issue, we grow a variety of microrganisms in ~100 different nutritional conditions and your metabolic potential is compared with genomic data. Thus, we are creating a high throughput analyses of phenotypes, and providing a rapid and rich metabolic modeling process across the microbial tree of life.